Wearable Installation Monitoring for Predictive Anomaly Response
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Solution Overview
Problem
Existing methods for managing technical installations, such as those in remote locations, struggle to accurately detect anomalies and determine appropriate mitigation actions, leading to potential catastrophic failures and damage due to the complexity of workflows and distance factors.
Innovation Solution
A method and system that uses predictive time series analysis of sensor data to detect anomalies, provide a holographic view of the affected area, and implement mitigation actions on wearable devices, enabling quick and informed decision-making to prevent disasters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used in remote technical installations, then the system can detect anomalies, but the ability to accurately identify the cause and location of anomalies deteriorates due to distance and workflow complexity
Solution Approach 1:
The system segments the complex technical installation into multiple portions or zones, each monitored by specific sensors. When an anomaly is detected, the system divides the analysis by examining sensor data from relevant portions separately, making the complex workflow manageable and improving anomaly location identification.
Solution Approach 2:
The system adds a spatial dimension to anomaly detection by using sensor networks distributed throughout the installation and wearable devices that provide location-aware monitoring. This dimensional approach allows precise localization of anomalies within the complex installation, transforming a difficult 2D problem into a more manageable multi-dimensional analysis.
2Reliability
If multiple sensors and monitoring systems are deployed to improve anomaly detection, then detection capability improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The system merges multiple sensor inputs, wearable device data, and analysis functions into a unified monitoring platform. By combining these elements into an integrated system, the complexity of operating multiple separate tools is reduced while maintaining high detection reliability through comprehensive data collection.
Solution Approach 2:
The system incorporates automated anomaly detection and analysis capabilities that operate autonomously without requiring constant human intervention. The wearable devices and sensor networks self-monitor and automatically generate alerts, reducing the operational burden on personnel while maintaining high reliability.
3Loss of information
If comprehensive sensor data collection is implemented to improve anomaly comprehension, then situational awareness improves, but data processing time and system response time increase
Solution Approach 1:
The system performs preliminary data processing and anomaly detection continuously in the background before actual anomalies occur. Sensor data is pre-analyzed and baseline patterns are established in advance, so when an anomaly occurs, the system can quickly compare against pre-computed patterns and provide immediate alerts without extensive real-time processing delays.
Solution Approach 2:
The system applies different processing qualities to different data sources based on their relevance. Critical sensors near the anomaly location receive intensive real-time analysis, while remote sensors use lighter processing. This localized quality approach ensures complete information gathering while minimizing overall processing time by focusing computational resources where most needed.
Data Source
AI summary
A method and system for managing a technical installation are disclosed. An event associated with at least a portion of the technical installation is detected based on sensor data associated with the portion of the technical installation. A representative view of the portion of the technical installation is rendered on at least one wearable device. The representative view displays information associated with the detected event in conjunction with the multi-dimensional view of the portion of the technical installation. A predictive time series analysis of the sensor data associated with the detected event is generated. The predictive time series analysis in conjunction with the representative view of the at least one portion of the technical installation is displayed.